Understanding a 3D CNN and Its Uses - MissingLink.ai

This layer is where images are translated into processable data by kernels, a filter layer consisting of learned parameters. Each kernel filters for a different feature and multiple kernels are used in each analysis. In a convolution, small areas of an image are scanned and the probability that they belong to a filter class is assigned and translated to an activation map, a representation of the image layers. In a 3D CNN, the kernels move through three dimensions of data (height, length, and depth) and produce 3D activation maps. Pooling, or downsampling, is done on the activation maps created during convolution.